Generation of synthetic context frameworks for dimensionally constrained hierarchical synthetic context-based objects

ABSTRACT

A processor-implemented method, system, and/or computer program product derives and utilizes a context object to generate a synthetic context-based object. A context object for a non-contextual data object is derived by contextually searching a document that contains multiple instances of the non-contextual data object. The non-contextual data object is associated with the derived context object to define a synthetic context-based object, where the non-contextual data object ambiguously relates to multiple subject-matters, and where the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object. The synthetic context-based object is then associated with at least one specific data store, which includes data that is associated with data contained in the non-contextual data object and the context object. A dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects is then constructed for handling requests for data stores.

BACKGROUND

The present disclosure relates to the field of computers, andspecifically to the use of databases in computers. Still moreparticularly, the present disclosure relates to a context-baseddatabase.

A database is a collection of data. Examples of database types includerelational databases, graph databases, network databases, andobject-oriented databases. Each type of database presents data in anon-dynamic manner, in which the data is statically stored.

SUMMARY

A processor-implemented method, system, and/or computer program productderives and utilizes a context object to generate a syntheticcontext-based object. A context object for a non-contextual data objectis derived by contextually searching a document that contains multipleinstances of the non-contextual data object. The non-contextual dataobject is associated with the derived context object to define asynthetic context-based object, where the non-contextual data objectambiguously relates to multiple subject-matters, and where the contextobject provides a context that identifies a specific subject-matter,from the multiple subject-matters, of the non-contextual data object.The synthetic context-based object is then associated with at least onespecific data store, which includes data that is associated with datacontained in the non-contextual data object and the context object. Adimensionally constrained hierarchical synthetic context-based objectlibrary for multiple synthetic context-based objects is then constructedfor handling requests for data stores.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 depicts an exemplary system and network in which the presentdisclosure may be implemented;

FIG. 2 illustrates a process for generating one or more syntheticcontext-based objects;

FIG. 3 depicts an exemplary case in which synthetic context-basedobjects are defined for the non-contextual data object datum “Rock”;

FIG. 4 illustrates an exemplary case in which synthetic context-basedobjects are defined for the non-contextual data object data “104-106”;

FIG. 5 depicts an exemplary case in which synthetic context-basedobjects are defined for the non-contextual data object datum “Statin”;

FIG. 6 illustrates a process for associating one or more data storeswith specific synthetic context-based objects;

FIG. 7 depicts a process for locating a particular data store via auser-selected synthetic context-based object;

FIG. 8 illustrates a horizontally constrained library of syntheticcontext-based objects according to non-contextual data objects;

FIG. 9 depicts a vertically-constrained hierarchical syntheticcontext-based object database;

FIG. 10 is a high-level flow chart of one or more steps performed by acomputer processor to generate and utilize a dimensionally constrainedhierarchical synthetic context-based object database;

FIG. 11 illustrates a process for locating a particular data store via auser-selected synthetic context-based object library; and

FIG. 12 is a high-level flow chart of one or more steps performed by acomputer processor to derive the context objects used by the presentdisclosure.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including, but not limited to, wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

In one embodiment, instructions are stored on a computer readablestorage device (e.g., a CD-ROM), which does not include propagationmedia.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thepresent invention. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

With reference now to the figures, and in particular to FIG. 1, there isdepicted a block diagram of an exemplary system and network that may beutilized by and in the implementation of the present invention. Notethat some or all of the exemplary architecture, including both depictedhardware and software, shown for and within computer 102 may be utilizedby software deploying server 150, a data storage system 152, and/or auser computer 154.

Exemplary computer 102 includes a processor 104 that is coupled to asystem bus 106. Processor 104 may utilize one or more processors, eachof which has one or more processor cores. A video adapter 108, whichdrives/supports a display 110, is also coupled to system bus 106. Systembus 106 is coupled via a bus bridge 112 to an input/output (I/O) bus114. An I/O interface 116 is coupled to I/O bus 114. I/O interface 116affords communication with various I/O devices, including a keyboard118, a mouse 120, a media tray 122 (which may include storage devicessuch as CD-ROM drives, multi-media interfaces, etc.), a printer 124, andexternal USB port(s) 126. While the format of the ports connected to I/Ointerface 116 may be any known to those skilled in the art of computerarchitecture, in one embodiment some or all of these ports are universalserial bus (USB) ports.

As depicted, computer 102 is able to communicate with a softwaredeploying server 150, using a network interface 130. Network interface130 is a hardware network interface, such as a network interface card(NIC), etc. Network 128 may be an external network such as the Internet,or an internal network such as an Ethernet or a virtual private network(VPN).

A hard drive interface 132 is also coupled to system bus 106. Hard driveinterface 132 interfaces with a hard drive 134. In one embodiment, harddrive 134 populates a system memory 136, which is also coupled to systembus 106. System memory is defined as a lowest level of volatile memoryin computer 102. This volatile memory includes additional higher levelsof volatile memory (not shown), including, but not limited to, cachememory, registers and buffers. Data that populates system memory 136includes computer 102's operating system (OS) 138 and applicationprograms 144.

OS 138 includes a shell 140, for providing transparent user access toresources such as application programs 144. Generally, shell 140 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 140 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 140, also called a command processor, is generally thehighest level of the operating system software hierarchy and serves as acommand interpreter. The shell provides a system prompt, interpretscommands entered by keyboard, mouse, or other user input media, andsends the interpreted command(s) to the appropriate lower levels of theoperating system (e.g., a kernel 142) for processing. Note that whileshell 140 is a text-based, line-oriented user interface, the presentinvention will equally well support other user interface modes, such asgraphical, voice, gestural, etc.

As depicted, OS 138 also includes kernel 142, which includes lowerlevels of functionality for OS 138, including providing essentialservices required by other parts of OS 138 and application programs 144,including memory management, process and task management, diskmanagement, and mouse and keyboard management.

Application programs 144 include a renderer, shown in exemplary manneras a browser 146. Browser 146 includes program modules and instructionsenabling a world wide web (WWW) client (i.e., computer 102) to send andreceive network messages to the Internet using hypertext transferprotocol (HTTP) messaging, thus enabling communication with softwaredeploying server 150 and other computer systems.

Application programs 144 in computer 102's system memory (as well assoftware deploying server 150's system memory) also include a syntheticcontext-based object library logic (SCBOPL) 148. SCBOPL 148 includescode for implementing the processes described below, including thosedescribed in FIGS. 2-11. In one embodiment, computer 102 is able todownload SCBOPL 148 from software deploying server 150, including in anon-demand basis, wherein the code in SCBOPL 148 is not downloaded untilneeded for execution. Note further that, in one embodiment of thepresent invention, software deploying server 150 performs all of thefunctions associated with the present invention (including execution ofSCBOPL 148), thus freeing computer 102 from having to use its owninternal computing resources to execute SCBOPL 148.

The data storage system 152 stores an electronic data structure, whichmay be audio files, video files, website content, text files, etc. Inone embodiment, computer 102 contains the synthetic context-based objectdatabase described herein, while data storage system 152 contains thenon-contextual data object database, context object database, and datastructure described herein. For example, in one embodiment, syntheticcontext-based object database 202 depicted in FIG. 2 and/or thesynthetic context-based object database 900 depicted in FIG. 9 and FIG.11 is stored in a synthetic context-based object database storagesystem, which is part of the hard drive 134 and/or system memory 136 ofcomputer 102 and/or data storage system 152; non-contextual data objectdatabase 206 depicted in FIG. 2 is stored in a non-contextual dataobject database storage system, which is part of the hard drive 134and/or system memory 136 of computer 102 and/or data storage system 152;context object database 212 depicted in FIG. 2 is stored in a contextobject database storage system, which is part of the hard drive 134and/or system memory 136 of computer 102 and/or data storage system 152;and data structure 602 depicted in FIG. 6 is stored in a data structurestorage system, which is part of the hard drive 134 and/or system memory136 of computer 102 and/or data storage system 152.

Note that the hardware elements depicted in computer 102 are notintended to be exhaustive, but rather are representative to highlightessential components required by the present invention. For instance,computer 102 may include alternate memory storage devices such asmagnetic cassettes, digital versatile disks (DVDs), Bernoullicartridges, and the like. These and other variations are intended to bewithin the spirit and scope of the present invention.

Note that SCBOPL 148 is able to generate and/or utilize some or all ofthe databases depicted in the context-based system referenced in FIGS.2-11.

With reference now to FIG. 2, a process for generating one or moresynthetic context-based objects in a system 200 is presented. Note thatsystem 200 is a processing and storage logic found in computer 102and/or data storage system 152 shown in FIG. 1, which process, support,and/or contain the databases, pointers, and objects depicted in FIG. 2.

Within system 200 is a synthetic context-based object database 202,which contains multiple synthetic context-based objects 204 a-204 n(thus indicating an “n” quantity of objects, where “n” is an integer).Each of the synthetic context-based objects 204 a-204 n is defined by atleast one non-contextual data object and at least one context object.That is, at least one non-contextual data object is associated with atleast one context object to define one or more of the syntheticcontext-based objects 204 a-204 n. The non-contextual data objectambiguously relates to multiple subject-matters, and the context objectprovides a context that identifies a specific subject-matter, from themultiple subject-matters, of the non-contextual data object.

Note that the data in the context objects are not merely attributes ordescriptors of the data/objects described by the non-contextual dataobjects. Rather, the context objects provide additional informationabout the non-contextual data objects in order to give thesenon-contextual data objects meaning. Thus, the context objects do notmerely describe something, but rather they define what something is.Without the context objects, the non-contextual data objects containdata that is meaningless; with the context objects, the non-contextualdata objects become meaningful.

For example, assume that a non-contextual data object database 206includes multiple non-contextual data objects 208 r-208 t (thusindicating a “t” quantity of objects, where “t” is an integer). However,data within each of these non-contextual data objects 208 r-208 t byitself is ambiguous, since it has no context. That is, the data withineach of the non-contextual data objects 208 r-208 t is data that,standing alone, has no meaning, and thus is ambiguous with regards toits subject-matter. In order to give the data within each of thenon-contextual data objects 208 r-208 t meaning, they are given context,which is provided by data contained within one or more of the contextobjects 210 x-210 z (thus indicating a “z” quantity of objects, where“z” is an integer) stored within a context object database 212. Forexample, if a pointer 214 a points the non-contextual data object 208 rto the synthetic context-based object 204 a, while a pointer 216 apoints the context object 210 x to the synthetic context-based object204 a, thus associating the non-contextual data object 208 r and thecontext object 210 x with the synthetic context-based object 204 a(e.g., storing or otherwise associating the data within thenon-contextual data object 208 r and the context object 210 x in thesynthetic context-based object 204 a), the data within thenon-contextual data object 208 r now has been given unambiguous meaningby the data within the context object 210 x. This contextual meaning isthus stored within (or otherwise associated with) the syntheticcontext-based object 204 a.

Similarly, if a pointer 214 b associates data within the non-contextualdata object 208 s with the synthetic context-based object 204 b, whilethe pointer 216 c associates data within the context object 210 z withthe synthetic context-based object 204 b, then the data within thenon-contextual data object 208 s is now given meaning by the data in thecontext object 210 z. This contextual meaning is thus stored within (orotherwise associated with) the synthetic context-based object 204 b.

Note that more than one context object can give meaning to a particularnon-contextual data object. For example, both context object 210 x andcontext object 210 y can point to the synthetic context-based object 204a, thus providing compound context meaning to the non-contextual dataobject 208 r shown in FIG. 2. This compound context meaning providesvarious layers of context to the data in the non-contextual data object208 r.

Note also that while the pointers 214 a-214 b and 216 a-216 c arelogically shown pointing towards one or more of the syntheticcontext-based objects 204 a-204 n, in one embodiment the syntheticcontext-based objects 204 a-204 n actually point to the non-contextualdata objects 208 r-208 t and the context objects 210 x-210 z. That is,in one embodiment the synthetic context-based objects 204 a-204 n locatethe non-contextual data objects 208 r-208 t and the context objects 210x-210 z through the use of the pointers 214 a-214 b and 216 a-216 c.

Consider now an exemplary case depicted in FIG. 3, in which syntheticcontext-based objects are defined for the non-contextual data objectdata “Rock”. Standing alone, without any context, the word “rock” ismeaningless, since it is ambiguous and does not provide a reference toany particular subject-matter. That is, “rock” may refer to a stone, orit may be slang for a gemstone such as a diamond, or it may refer to agenre of music, or it may refer to physical oscillation, etc. Thus, eachof these references is within the context of a different subject-matter(e.g., geology, entertainment, physics, etc.).

In the example shown in FIG. 3, then, data (i.e., the word “rock”) fromthe non-contextual data object 308 r is associated with (e.g., stored inor associated by a look-up table, etc.) a synthetic context-based object304 a, which is devoted to the subject-matter “geology”. The data/word“rock” from non-contextual data object 308 r is also associated with asynthetic context-based object 304 b, which is devoted to thesubject-matter “entertainment”. In order to give contextual meaning tothe word “rock” (i.e., define the term “rock”) in the context of“geology”, context object 310 x, which contains the context datum“mineral” is associated with (e.g., stored in or associated by a look-uptable, etc.) the synthetic context-based object 304 a. In oneembodiment, more than one context datum can be associated with a singlesynthetic context-based object. Thus, in the example shown in FIG. 3,the context object 310 y, which contains the datum “gemstone”, is alsoassociated with the synthetic context-based object 304 a.

Associated with the synthetic context-based object 304 b is a contextobject 310 z, which provides the context/datum of “music” to the term“rock” provided by the non-contextual data object 308 r. Thus, thesynthetic context-based object 304 a defines “rock” as that which isrelated to the subject-matter “geology”, including minerals and/orgemstones, while synthetic context-based object 304 b defines “rock” asthat which is related to the subject-matter “entertainment”, includingmusic.

In one embodiment, the data within a non-contextual data object is evenmore meaningless if it is merely a combinations of numbers and/orletters. For example, consider the data “104-106” contained within anon-contextual data object 408 r depicted in FIG. 4. Standing alone,without any context, these numbers are meaningless, identify noparticular subject-matter, and thus are completely ambiguous. That is,“104-106” may relate to subject-matter such as a medical condition, aphysics value, a person's age, a quantity of currency, an person'sidentification number, etc. That is, the data “104-106” is sovague/meaningless that the data does not even identify the units thatthe term describes, much less the context of these units.

In the example shown in FIG. 4, then, data (i.e., the term/values“104-106”) from the non-contextual data object 408 r is associated with(e.g., stored in or associated by a look-up table, etc.) a syntheticcontext-based object 404 a, which is devoted to the subject-matter“hypertension”. The term/values “104-106” from non-contextual dataobject 408 r is also associated with a synthetic context-based object404 b, which is devoted to the subject-matter “human fever” and asynthetic context-based object 404 n, which is devoted to thesubject-matter “deep oceanography”. In order to give contextual meaningto the term/values “104-106” (i.e., define the term/values “104-106”) inthe context of “hypertension”, context object 410 x, which contains thecontext data “millimeters of mercury” and “diastolic blood pressure” isassociated with (e.g., stored in or associated by a look-up table, etc.)the synthetic context-based object 404 a. Thus, multiple data canprovide not only the scale/units (millimeters of mercury) context of thevalues “104-106”, but the data can also provide the context data“diastolic blood pressure” needed to identify the subject-matter(hypertension) of the synthetic context-based object 404 a.

Associated with the synthetic context-based object 404 b is a contextobject 410 y, which provides the context/data of “degrees on theFahrenheit scale” and “human” to the term/values “104-106” provided bythe non-contextual data object 408 r. Thus, the synthetic context-basedobject 404 b now defines term/values “104-106” as that which is relatedto the subject matter of “human fever”. Similarly, associated with thesynthetic context-based object 404 n is a context object 410 z, whichprovides the context/data of “deep oceanography” to the term/values“104-106” provided by the non-contextual data object 408 r. In thiscase, the generator of the synthetic context-based object database 202determines that high numbers of atmospheres are used to define deepocean pressures. Thus, the synthetic context-based object 404 n nowdefines term/values “104-106” as that which is related to the subjectmatter of deep oceanography.

In one embodiment, the non-contextual data object may provide enoughself-context to identify what the datum is, but not what it means and/oris used for. For example, consider the datum “statin” contained withinthe non-contextual data object 508 r shown in FIG. 5. In the exampleshown in FIG. 5, datum (i.e., the term “statin”) from the non-contextualdata object 508 r is associated with (e.g., stored in or associated by alook-up table, etc.) a synthetic context-based object 504 a, which isdevoted to the subject-matter “cardiology”. The term “statin” fromnon-contextual data object 508 r is also associated with a syntheticcontext-based object 504 b, which is devoted to the subject-matter“nutrition” and a synthetic context-based object 504 n, which is devotedto the subject-matter “tissue inflammation”. In order to give contextualmeaning to the term “statin” (i.e., define the term “statin”) in thecontext of “cardiology”, context object 510 x, which contains thecontext data “cholesterol reducer” is associated with (e.g., stored inor associated by a look-up table, etc.) the synthetic context-basedobject 504 a. Thus, the datum “cholesterol reducer” from context object510 x provides the context to understand that “statin” is used in thecontext of the subject-matter “cardiology”.

Associated with the synthetic context-based object 504 b is a contextobject 510 y, which provides the context/datum of “antioxidant” to theterm “statin” provided by the non-contextual data object 508 r. That is,a statin has properties both as a cholesterol reducer as well as anantioxidant. Thus, a statin can be considered in the context of reducingcholesterol (i.e., as described by the subject-matter of syntheticcontext-based object 504 a), or it may considered in the context ofbeing an antioxidant (i.e., as related to the subject-matter ofsynthetic context-based object 504 b). Similarly, a statin can also bean anti-inflammatory medicine. Thus, associated with the syntheticcontext-based object 504 b is a context object 510 y, which provides thecontext/data of “antioxidant” to the term “statin” provided by thenon-contextual data object 508 r. This combination identifies thesubject-matter of the synthetic context-based object 504 b as “tissueinflammation”. Similarly, associated with the synthetic context-basedobject 504 n is the context object 510 z, which provides thecontext/data of “anti-inflammatory medication” to the term “statin”provided by the non-contextual data object 508 r. This combinationidentifies the subject-matter of the synthetic context-based object 504n as “tissue inflammation”.

Once the synthetic context-based objects are defined, they can be linkedto data stores. A data store is defined as a data repository of a set ofintegrated data, such as text files, video files, webpages, etc. Withreference now to FIG. 6, a process for associating one or more datastores with specific synthetic context-based objects in a system 600 ispresented. Note that system 600 is a processing and storage logic foundin computer 102 and/or data storage system 152 shown in FIG. 1, whichprocess, support, and/or contain the databases, pointers, and objectsdepicted in FIG. 6. The data structure 604 is a database of multipledata stores 602 m-602 p (thus indicating an “p” number of data stores,where “p” is an integer), which may be text documents, hierarchicalfiles, tuples, object oriented database stores, spreadsheet cells,uniform resource locators (URLs), etc.

That is, in one embodiment, the data structure 604 is a database of textdocuments (represented by one or more of the data stores 602 m-602 p),such as journal articles, webpage articles, electronically-storedbusiness/medical/operational notes, etc.

In one embodiment, the data structure 604 is a database of text, audio,video, multimedia, etc. files (represented by one or more of the datastores 602 m-602 p) that are stored in a hierarchical manner, such as ina tree diagram, a lightweight directory access protocol (LDAP) folder,etc.

In one embodiment, the data structure 604 is a relational database,which is a collection of data items organized through a set of formallydescribed tables. A table is made up of one or more rows, known as“tuples”. Each of the tuples (represented by one or more of the datastores 602 m-602 p) share common attributes, which in the table aredescribed by column headings. Each tuple also includes a key, which maybe a primary key or a foreign key. A primary key is an identifier (e.g.,a letter, number, symbol, etc.) that is stored in a first data cell of alocal tuple. A foreign key is typically identical to the primary key,except that it is stored in a first data cell of a remote tuple, thusallowing the local tuple to be logically linked to the foreign tuple.

In one embodiment, the data structure 604 is an object orienteddatabase, which stores objects (represented by one or more of the datastores 602 m-602 p). As understood by those skilled in the art ofcomputer software, an object contains both attributes, which are data(i.e., integers, strings, real numbers, references to another object,etc.), as well as methods, which are similar to procedures/functions,and which define the behavior of the object. Thus, the object orienteddatabase contains both executable code and data.

In one embodiment, the data structure 604 is a spreadsheet, which ismade up of rows and columns of cells (represented by one or more of thedata stores 602 m-602 p). Each cell (represented by one or more of thedata stores 602 m-602 p) contains numeric or text data, or a formula tocalculate a value based on the content of one or more of the other cellsin the spreadsheet.

In one embodiment, the data structure 604 is a collection of universalresource locators (URLs) for identifying a webpage, in which each URL(or a collection of URLs) is represented by one or more of the datastores 602 m-602 p.

These described types of data stores are exemplary, and are not to beconstrued as limiting what types of data stores are found within datastructure 604.

Note that the data structure 604 is homogenous in one embodiment, whiledata structure 604 is heterogeneous in another embodiment. For example,assume in a first example that data structure 604 is a relationaldatabase, and all of the data stores 602 m-602 p are tuples. In thisfirst example, data structure 604 is homogenous, since all of the datastores 602 m-602 p are of the same type. However, assume in a secondexample that data store 602 m is a text document, data store 602 m is anMRI image, data store 602 p is a tuple from a relational database, etc.In this second example, data structure 604 is a heterogeneous datastructure, since it contains data stores that are of different formats.

FIG. 6 thus represents various data stores being “laid over” one or moreof the synthetic context-based objects 304 a-304 n described above inFIG. 3. That is, one or more of the data stores 602 m-602 p is mapped toa particular synthetic context-based object from the syntheticcontext-based objects 304 a-304 n, in order to facilitateexploring/searching the data structure 604. For example, a pointer 606(e.g., an identifier located within both synthetic context-based object304 a and data store 602 m) points the data store 602 m to the syntheticcontext-based object 304 a, based on the fact that the data store 602 mcontains data found in the non-contextual data object 208 r and thecontext object 210 x, which together gave the subject-matter meaning tothe synthetic context-based object 304 a as described above. Similarly,pointer 608 points data store 602 n to synthetic context-based object304 a as well, provided that synthetic context based object 304 a alsocontains data from context object 210 y, as described in an alternateembodiment above. Similarly, pointer 610 points data store 602 p tosynthetic context-based object 304 b, since data store 602 p andsynthetic context-based object 304 b both contain data from thenon-contextual data object 208 r as well as the context object 210 z.

As described in FIG. 6, the pointers enable various data stores to beassociated with specific subject-matter-specific synthetic context basedobjects. This association facilitates searching the data structure 604according to the subject-matter, which is defined by the combination ofdata from the non-contextual data object and the context object, of aparticular synthetic context-based object. Thus, as depicted in FIG. 7,an exemplary process for locating a particular data store via aparticular synthetic context-based object is presented.

Assume that a user is using a computer such as requesting computer 702,which may be the user computer 154 shown in FIG. 1. The requestingcomputer 702 sends a request 704 to synthetic context-based object 304 aif the user desires information about geological rocks (i.e., thesubject-matter of geology). The user can specify this particularcontext-based object 304 a by manually choosing it from a displayedselection of synthetic context-based objects, or logic (e.g., part ofSCBOPL 148 shown in FIG. 1) can determine which synthetic context-basedobject and/or subject-matter are appropriate for a particular user,based on that user's interests, job description, job title, etc. Thesynthetic context-based object then uses pointer 606 to point to datastore 602 m and/or pointer 608 to point to data store 602, and returnsthe data stored within these data stores to the requesting computer 702.Thus, the user/requesting system does not have to perform a search ofall of the data structure 604, using data mining and associative logic,in order to find the data that the user desires. Rather, making anassociation between the user and a particular synthetic context-basedobject provides a rapid gateway from the requesting computer 702 to thedesired data store.

Similarly, if the requester sends a request 706 to the syntheticcontext-based object 304 b, then data from the data store 602 pregarding rock music is retrieved and sent to the requester 702.

Note that in one embodiment of the present invention, a library ofsynthetic context-based objects is constructed to facilitate the user ofthe synthetic context-based objects when searching a data structure. Inone embodiment, this library is horizontally constrained, such thatsynthetic context-based objects within a same dimension are placedwithin a same library. For example, consider the synthetic context-basedobject database 800 depicted in FIG. 8. Within a first horizontallibrary 812 are synthetic context-based objects 804 a-804 c. Each ofthese synthetic context-based objects 804 a-804 c contains a samenon-contextual data object 808 r, but they have different contextobjects 810 x-810 z, as depicted. Within a second horizontal library 814are synthetic context-based objects 804 d-804 f. Each of these syntheticcontext-based objects 804 d-804 f contain a same non-contextual dataobject 808 s, but they have different context objects 810 a-810 c, whichmay the same or different context objects as context objects 810 x-810z.

The synthetic context-based object database 900 depicted in FIG. 9depicts libraries that are organized according to the context objects,rather than the non-contextual data objects. That is, a first verticallibrary 922 contains synthetic context-based objects 904 a-904 c. Eachof these synthetic context-based objects 904 a-904 c contains differentnon-contextual data objects 908 r and 908 s, but they have the samecontext object 910 x, as depicted. Within a second vertical library 924are synthetic context-based objects 904 c-904 d, which contain differentnon-contextual data object 908 t and 908 v, but they have the samecontext object 910 y. Similarly, within a third vertical library 926 aresynthetic context-based objects 904 e-904 f, which contain differentnon-contextual data object 908 w and 908 x, but they have the samecontext object 910 x.

Thus, it is the presence of the same non-contextual data object in asynthetic context-based object that defines the horizontal library,while it is the presence of the same context object in a syntheticcontext-based object that defines the vertical library.

With reference now to FIG. 10, a high-level flow chart of one or moresteps performed by a computer processor to generate and utilizesynthetic context-based objects to locate and/or return specific datastores to a requester is presented. After initiator block 1002, anon-contextual data object is associated with a context object to definea synthetic context-based object (block 1004). As described herein, thenon-contextual data object ambiguously relates to multiplesubject-matters. Standing alone, it is unclear to which of thesemultiple-subject matters the data in the non-contextual data object isdirected. However, the context object provides a context that identifiesa specific subject-matter, from the multiple subject-matters, of thenon-contextual data object.

As described in block 1006, the synthetic context-based object isassociated with at least one specific data store. This at least onespecific data store contains data that is associated with data containedin the non-contextual data object and the context object. That is, thedata in the data store may be identical to that found in thenon-contextual data object and the context object (i.e., the terms“rock” and “mineral” are in both the data store as well as therespective non-contextual data object and context object); it may besynonymous to that found in the non-contextual data object and thecontext object (i.e., the terms “rock” and “mineral” are the respectivenon-contextual data object and context object while synonyms “stone” and“element” are in the data store); and/or it may simply be deemed relatedby virtue of a lookup table that has been previously created (i.e., theterm “rock” is mapped to the term “stone” and/or the term “mineral” ismapped to the term “elements” in a lookup table or similar associativedata structure).

In one embodiment, the terms in the data store are identified by datamining a data structure in order to locate the data from thenon-contextual data object and the context object in one or more datastores. Thus, this data mining locates at least one specific data storethat contains data contained in the non-contextual data object and thecontext object.

In one embodiment, the data store is a text document. In thisembodiment, the data mining entails searching the text document for textdata that is part of the synthetic context-based object, and thenassociating the text document that contains this text data with thesynthetic context-based object.

In one embodiment, the data store is a video file. In this embodiment,the data mining entails searching metadata associated with the videofile for text data that is part of the synthetic context-based object,and then associating the video file having this metadata with thesynthetic context-based object.

In one embodiment, the data store is a web page. In this embodiment, thedata mining entails searching the web page for text data that is part ofthe synthetic context-based object, and then associating the web pagethat contains this text data with the synthetic context-based object.

Note that in one embodiment, the specific subject-matter for aparticular data store in the data structure is exclusive to only thatparticular data store. That is, only one data store is mapped to aparticular synthetic context-based object, such that there is aone-to-one relationship between each synthetic context-based object andeach data store. Note further that in another embodiment, the specificsubject-matter for a particular data store in the data structureoverlaps at least one other data store. That is, multiple data storesare mapped to a particular synthetic context-based object, such thatthere is a one-to-many relationship between a particular syntheticcontext-based object and multiple data stores.

With reference now to block 1008, a dimensionally constrainedhierarchical synthetic context-based object library for multiplesynthetic context-based objects is then constructed, where syntheticcontext-based objects within a same dimension of the dimensionallyconstrained hierarchical synthetic context-based object library sharedata. If the shared data is from a same non-contextual data object, thenthe dimensionally constrained hierarchical synthetic context-basedobject library is a horizontal library, in which synthetic context-basedobjects within the same horizontal dimension of the dimensionallyconstrained hierarchical synthetic context-based object library containdisparate data from different context objects. If the shared data isfrom a same context object, then the dimensionally constrainedhierarchical synthetic context-based object library is a verticallibrary, in which synthetic context-based objects within the samevertical dimension of the dimensionally constrained hierarchicalsynthetic context-based object library contain disparate data fromdifferent non-contextual data objects.

With reference now to block 1010, a request for at least one data storethat is associated with synthetic context-based objects within the samedimension of the dimensionally constrained hierarchical syntheticcontext-based object library is then received (e.g., by computer 102shown in FIG. 1). In one embodiment, this request is received from therequester via a request pointer, which points to a specific syntheticcontext-based object. In one embodiment, this specific syntheticcontext-based object is user-selected and/or user-specified (i.e., theuser either manually chooses which synthetic context-based object is tobe used, or this choice is made by processing logic based oncharacteristics of the requesting user). For example, consider theprocess depicted in FIG. 11.

The requesting computer 702 sends a request 1104 to the first verticallibrary 922 described in FIG. 9, rather than to the syntheticcontext-based object 304 a described in FIG. 7. This first verticallibrary 922 contains only synthetic context-based objects that share asame context object 910 x (as shown in FIG. 9). Assuming that datawithin context object 910 x refers to “minerals”, then pointer 1110points from the first vertical library 922 to the data store 602 m,which has data about “quartz”. Similarly, the second vertical library924 contains only synthetic context-based objects that share a samecontext object 910 y (as shown in FIG. 9). Assuming that data withincontext object 910 y refers to “gemstones”, then pointer 1112 pointsfrom the second vertical library 924 to the data store 602 n, which hasdata about “diamonds”. Finally, the third vertical library 926 containsonly synthetic context-based objects that share a same context object910 z (as shown in FIG. 9). Assuming that data within context object 910z refers to “music”, then pointer 1114 points from the third verticallibrary 926 to the data store 602 p, which has data about “rock music”.In one embodiment, the user can specify a particular vertical library bymanually choosing it from a displayed selection of vertical objects, orlogic (e.g., part of SCBOPL 148 shown in FIG. 1) can determine whichvertical and/or subject-matter/context are appropriate for a particularuser, based on that user's interests, job description, job title, etc.

As described in block 1012, at least one specific data store that isassociated with synthetic context-based objects within the samedimension of the dimensionally constrained hierarchical syntheticcontext-based object library are then returned to the requester. In oneembodiment, pointers similar to (or the same as) pointers 1110, 1112,and 1114 are used to return the data in these data stores to therequesting computer. Thus, the user/requesting system does not have toperform a search of all of the data structure 604, using data mining andassociative logic, in order to find the data that the user desires.Rather, making an association between the user and a particular verticallibrary of synthetic context-based objects provides a rapid gateway fromthe requesting computer 702 to the desired data store.

The process ends at terminator block 1014.

As described herein, the present process utilizes one or more contextobjects. An exemplary process, executed by a processor, for deriving therequisite context objects is presented in FIG. 12. After initiator block1202, a minimum validity threshold for the context object is established(block 1204). This minimum validity threshold defines a probability thata set of one or more context objects accurately describes the context ofa particular non-contextual data object. As described in block 1206, adocument, which contains multiple instances of this particularnon-contextual data object, is then searched and analyzed in order todetermine the context of this non-contextual data object. For example,assume that context object 410 y shown in FIG. 4 has an 80% probabilityof accurately defining the context of “104-106” as being a that of anabnormally high human oral temperature, based on a contextual search andanalysis of a single paragraph from a book. However, if the minimumvalidity threshold is 95% (see query block 1208), then the rest of apage from the book is further searched/analyzed (block 1210). If thisminimum validity threshold is still not reached, then a full chapter, orperhaps even the entire book, is contextually searched and analyzed inorder to reach the minimum validity threshold (block 1212). The processends at terminator block 1214.

In one embodiment, two context objects within a document may be deemedsimilarly appropriate for one non-contextual data object. Asearch/analysis of the document may reveal enough instances of both afirst context object and a second context object to satisfy the minimumvalidity threshold for assigning a context object to a non-contextualdata object to create an appropriate synthetic context-based object. Inthis embodiment, the processor may assign a correct context object tothe non-contextual data object based on which context object (of thefirst and second context objects) appears most often in the document(i.e., which context object is used more frequently). The context objectwith the greater number of instances of use in the document is thereforedetermined to be the correct context object to define a syntheticcontext-based object based on a particular non-contextual data object.

Alternatively, in one embodiment, if two context objects are determinedto both meet the minimum validity threshold for defining a singlenon-contextual data object, the processor may determine that both orneither of the resulting synthetic context-based objects areappropriate. That is, rather than requiring that a single context objectbe chosen as the correct context object for a particular non-contextualdata object, both context objects that meet the minimum validitythreshold are assigned to that particular non-contextual data object bythe processor. In another embodiment, the processor may insteaddetermine that neither context object is the correct context object forthat particular non-contextual data object (despite both context objectsmeeting the minimum validity threshold) and require that a third contextobject, that better meets the minimum validity threshold requirements,be searched for, in order to create a synthetic context-based objectthat more accurately describes the context of the data.

In one embodiment, the search/analysis of the document (i.e., a textdocument, video file, audio file, etc.) is performed by contextuallyexamining the data proximate to the non-contextual data object withinthe document. For example, assume that the non-contextual data object is“3.14”. Within one paragraph, “3.14” is used as part of a math formula.Therefore, “3.14” is initially assumed to be relevant to calculating thearea or circumference of a circle. However, in another paragraph withinthe document, “3.14” is used to describe a price of a stock. Additionalinstances of “3.14” are also used within passages related to businessinvestments and finance. Thus, the original assumption that “3.14”describes a number used to calculate the area or circumference of acircle was incorrect. However, if the minimum validity threshold werelow enough (e.g., 50%), then a context object of “circle area” or“circle circumference” would likely be assigned to “3.14”. However, ifthe minimum validity threshold were higher (e.g., 95%), then the singleassociation of “3.14” with a math formula would not reach this level,thus requiring that the scope of search of the document be expanded,such that the proper context object (i.e., “stock price”) is derived.

Note that if the document is a video or audio file, the document issearched by first performing audio-to-text conversion, and thensearching for the text.

In one embodiment, the expanded search of the document is performedusing a Map/Reduce function. Map/Reduce is a data search routine that isused to search for and quantify data located in very large volumes ofdata. As the name implies, there are two functions in a map/reduceroutine. The map function reads data incidents (e.g., each word in atext document) from a set of one or more documents (e.g., textdocuments, web pages, e-mails, text messages, tweets, etc.), and thenmaps those data incidents to a set of dynamically generated intermediatepairs (identifier, quantity). These intermediate pairs of mapped dataare then sent to a reduce function, which recursively operates on theintermediate pairs to generate a value that indicates how many timeseach data incident occurred in all of the searched documents.

In one embodiment in which Map/Reduce is used to derive the contextobject, a progressively higher number of processors are used to executethe evaluations of the subsequently lower-level partitions of thedocument. For example, assume that a first pass at evaluating a singleline in a document uses a single processor. If this first pass does notproduce a context object that meets the minimum validity thresholddescribed above, then the full paragraph is evaluated by four processorsin a second pass. If this second pass does not produce a context objectthat meets the minimum validity threshold, then sixteen processors areassigned to evaluate the entire chapter, etc.

In one embodiment, the minimum validity threshold described herein isnot for a single context object, but for a set of multiple contextobjects. For example, assume that context objects 210 x and 210 y bothprovide context to non-contextual data object 208 r depicted in FIG. 2.In this embodiment, it is the combination of both context objects 210 xand 210 y that is used to determine if they meet the minimum validitythreshold.

In one embodiment, the probability that a set of one or more contextobjects accurately provides context to a non-contextual data object ishistorically based. That is, assume that in 95% of past documents, aparticular context object has been shown to provide the accurate context(i.e., provides correct meaning) to a particular non-contextual dataobject if that particular context object is used in proximity to (i.e.,within 10 words) that particular non-contextual data object more thanthree times. Thus, if this particular context object is used more thanthree times in proximity to this particular non-contextual data objectwithin the current document, then there is an assumption that thisparticularly context object has a 95% probability (likelihood) ofaccurately providing the proper context to this particularnon-contextual data object.

In one embodiment, the probability that a set of one or more contextobjects accurately provides context to a non-contextual data object isestablished using Bayesian probabilities. For example, consider theBayesian probability formula of:

${P\left( {{CO}D} \right)} = \frac{{P\left( {D{CO}} \right)}*{P({CO})}}{P(D)}$

where:P(CO|D) is a probability that the context object (CO) provides anaccurate context to the non-contextual data object given (|) that thecontext object is within ten words of the non-contextual data objectmore than a certain number of times (D) in the document;P(D|CO) is a probability that the context object is within ten words ofthe non-contextual data object more than a certain number of times inthe document whenever the context object provides the accurate contextto the non-contextual data object;P(CO) is a probability that context object provides an accurate contextto the non-contextual data object, regardless of any other conditions;andP(D) is a probability that the non-contextual data object is within tenwords of the non-contextual data object more than a certain number oftimes in the document, regardless of any other conditions.

For example, assume that past evaluations have determined that theprobability that the context object is within ten words of thenon-contextual data object more than a certain number of times in aparagraph of a book whenever the context object provides the accuratecontext to the non-contextual data object is 60%; the probability thatcontext object provides an accurate context to the non-contextual dataobject, regardless of any other conditions, is 5%; and the probabilitythat the non-contextual data object is within ten words of thenon-contextual data object more than a certain number of times in thedocument, regardless of any other conditions, is 10%.

According to these values, the probability that a context objectsprovide an accurate context to the non-contextual data object given thatthis context object is within ten words of the non-contextual dataobject more than a certain number of times in the single paragraph is3%:

${P\left( {{CO}D} \right)} = {\frac{{.60}*{.05}}{.1} = {.3}}$

In this first scenario, if the minimum validity threshold is 95%, thenthere is not enough support to assume that this context object is valid.Thus, a further search for the context object within ten words of thenon-contextual data object is made on an entire page/chapter. In thisscenario, past evaluations have determined that the probability that thecontext object is within ten words of the non-contextual data objectmore than a certain number of times in an entire page/chapter of a bookwhenever the context object provides the accurate context to thenon-contextual data object is 78%; the probability that context objectprovides an accurate context to the non-contextual data object,regardless of any other conditions, is still 5%; and the probabilitythat the non-contextual data object is within ten words of thenon-contextual data object more than a certain number of times in thedocument, regardless of any other conditions, is now 4%.

According to these values, the probability that a context objectprovides an accurate context to the non-contextual data object giventhat this context object is within ten words of the non-contextual dataobject more than a certain number of times in the entire page/chapter is98%:

${P\left( {{CO}D} \right)} = {\frac{{.78}*{.05}}{.04} = {.98}}$

In this second scenario, the additional search/analysis of the entirepage or chapter deems this particular context object to accuratelyprovide context to the non-contextual data object.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentinvention. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of various embodiments of the present invention has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the present invention in theform disclosed. Many modifications and variations will be apparent tothose of ordinary skill in the art without departing from the scope andspirit of the present invention. The embodiment was chosen and describedin order to best explain the principles of the present invention and thepractical application, and to enable others of ordinary skill in the artto understand the present invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

Note further that any methods described in the present disclosure may beimplemented through the use of a VHDL (VHSIC Hardware DescriptionLanguage) program and a VHDL chip. VHDL is an exemplary design-entrylanguage for Field Programmable Gate Arrays (FPGAs), ApplicationSpecific Integrated Circuits (ASICs), and other similar electronicdevices. Thus, any software-implemented method described herein may beemulated by a hardware-based VHDL program, which is then applied to aVHDL chip, such as a FPGA.

Having thus described embodiments of the present invention of thepresent application in detail and by reference to illustrativeembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of the presentinvention defined in the appended claims.

What is claimed is:
 1. A method for deriving and utilizing a contextobject to generate a synthetic context-based object, the methodcomprising: deriving, by one or more processors, a context object for anon-contextual data object, wherein the non-contextual data objectambiguously relates to multiple subject-matters, wherein the contextobject provides a context that identifies a specific subject-matter,from multiple subject-matters, of the non-contextual data object, andwherein the context object is derived by contextually searching andanalyzing a document, which contains multiple instances of thenon-contextual data object, to derive the context object; establishing aminimum validity threshold for the context object, wherein the minimumvalidity threshold defines a probability that a derived context objectaccurately describes the context of the non-contextual data object;expanding a range of a search area of the document until the minimumvalidity threshold is reached. associating, by one or more processors,the non-contextual data object with the context object to define asynthetic context-based object; associating, by one or more processors,the synthetic context-based object with at least one specific datastore, wherein said at least one specific data store comprises data thatis associated with data contained in the non-contextual data object andthe context object; constructing, by one or more processors, adimensionally constrained hierarchical synthetic context-based objectlibrary for multiple synthetic context-based objects, wherein syntheticcontext-based objects within a same dimension of the dimensionallyconstrained hierarchical synthetic context-based object library sharedata from a same non-contextual data object, and wherein syntheticcontext-based objects within the same dimension of the dimensionallyconstrained hierarchical synthetic context-based object library containdisparate data from different context objects; receiving, from arequester, a request for at least one data store that is associated withsynthetic context-based objects within the same dimension of thedimensionally constrained hierarchical synthetic context-based objectlibrary; and returning, to the requester, said at least one specificdata store that is associated with synthetic context-based objectswithin the same dimension of the dimensionally constrained hierarchicalsynthetic context-based object library.
 2. The method of claim 1,wherein the document is a text document.
 3. The method of claim 1,wherein the document is an audio file.
 4. The method of claim 1, whereinthe document is a video file.
 5. The method of claim 1, furthercomprising: executing, by one or more processors, a map/reduce search ofthe document to reach the minimum validity threshold, wherein themap-reduce search sequentially performs evaluations of subsequentlylower-level partitions of the document to determine finer levels ofgranularity to derive the context object.
 6. The method of claim 5,further comprising: utilizing a progressively higher number ofprocessors to execute the evaluations of the subsequently lower-levelpartitions of the document.
 7. The method of claim 1, furthercomprising: determining, by one or more processors, a probability thatthe context object accurately describes the context of thenon-contextual data object through use of a probability formula of:${P\left( {{CO}D} \right)} = \frac{{P\left( {D{CO}} \right)}*{P({CO})}}{P(D)}$where: P(CO|D) is a probability that the context object (CO) provides anaccurate context to the non-contextual data object given (|) that thecontext object is within ten words of the non-contextual data objectmore than a certain number of times (D) in the document; P(D|CO) is aprobability that the context object is within ten words of thenon-contextual data object more than a certain number of times in thedocument whenever the context object provides the accurate context tothe non-contextual data object; P(CO) is a probability that contextobject provides an accurate context to the non-contextual data object,regardless of any other conditions; and P(D) is a probability that thenon-contextual data object is within ten words of the non-contextualdata object more than a certain number of times in the document,regardless of any other conditions.
 8. The method of claim 1, whereinthe specific subject-matter for a particular data store in the datastructure is exclusive to only said particular data store.
 9. The methodof claim 1, wherein the specific subject-matter for a particular datastore in the data structure overlaps a subject-matter of another datastore in the data structure.
 10. The method of claim 1, wherein the datastore is a text document, and wherein the method further comprises:searching, by one or more processors, the text document for text datathat is part of the synthetic context-based object; and associating, byone or more processors, the text document that contains said text datawith the synthetic context-based object.
 11. The method of claim 1,wherein the data store is a video file, and wherein the method furthercomprises: searching, by one or more processors, metadata associatedwith the video file for text data that is part of the syntheticcontext-based object; and associating, by one or more processors, thevideo file having said metadata with the synthetic context-based object.12. The method of claim 1, wherein the data store is a web page, andwherein the method further comprises: searching, by one or moreprocessors, the web page for text data that is part of the syntheticcontext-based object; and associating, by one or more processors, theweb page that contains said text data with the synthetic context-basedobject.
 13. The method of claim 1, further comprising: receiving therequest from the requester via a request pointer, wherein the requestpointer points to a user-specified synthetic context-based object.
 14. Acomputer program product for deriving and utilizing a context object togenerate a synthetic context-based object, the computer program productcomprising a computer readable storage medium having program codeembodied therewith, the program code readable and executable by aprocessor to perform a method comprising: deriving a context object fora non-contextual data object, wherein the non-contextual data objectambiguously relates to multiple subject-matters, wherein the contextobject provides a context that identifies a specific subject-matter,from multiple subject-matters, of the non-contextual data object, andwherein the context object is derived by contextually searching andanalyzing a document, which contains multiple instances of thenon-contextual data object, to derive the context object; establishing aminimum validity threshold for the context object, wherein the minimumvalidity threshold defines a probability that a derived context objectaccurately describes the context of the non-contextual data object;expanding a range of a search area of the document until the minimumvalidity threshold is reached. associating the non-contextual dataobject with the context object to define a synthetic context-basedobject; associating the synthetic context-based object with at least onespecific data store, wherein said at least one specific data storecomprises data that is associated with data contained in thenon-contextual data object and the context object; constructing adimensionally constrained hierarchical synthetic context-based objectlibrary for multiple synthetic context-based objects, wherein syntheticcontext-based objects within a same dimension of the dimensionallyconstrained hierarchical synthetic context-based object library sharedata from a same non-contextual data object, and wherein syntheticcontext-based objects within the same dimension of the dimensionallyconstrained hierarchical synthetic context-based object library containdisparate data from different context objects; receiving, from arequester, a request for at least one data store that is associated withsynthetic context-based objects within the same dimension of thedimensionally constrained hierarchical synthetic context-based objectlibrary; and returning, to the requester, said at least one specificdata store that is associated with synthetic context-based objectswithin the same dimension of the dimensionally constrained hierarchicalsynthetic context-based object library.
 15. The computer program productof claim 14, wherein the program code is further readable and executableby the processor for: executing a map/reduce search of the document toreach the minimum validity threshold, wherein the map-reduce searchsequentially performs evaluations of subsequently lower-level partitionsof the document to determine finer levels of granularity to derive thecontext object; and utilizing a progressively higher number ofprocessors to execute the evaluations of the subsequently lower-levelpartitions of the document.
 16. The computer program product of claim14, wherein the document is a video file.
 17. The computer programproduct of claim 14, wherein the program code is further readable andexecutable by the processor for: determining a probability that thecontext object accurately describes the context of the non-contextualdata object through use of a probability formula of:${P\left( {{CO}D} \right)} = \frac{{P\left( {D{CO}} \right)}*{P({CO})}}{P(D)}$where: P(CO|D) is a probability that the context object (CO) provides anaccurate context to the non-contextual data object given (|) that thecontext object is within ten words of the non-contextual data objectmore than a certain number of times (D) in the document; P(D|CO) is aprobability that the context object is within ten words of thenon-contextual data object more than a certain number of times in thedocument whenever the context object provides the accurate context tothe non-contextual data object; P(CO) is a probability that contextobject provides an accurate context to the non-contextual data object,regardless of any other conditions; and P(D) is a probability that thenon-contextual data object is within ten words of the non-contextualdata object more than a certain number of times in the document,regardless of any other conditions.
 18. A computer system comprising: aprocessor, a computer readable memory, and a computer readable storagemedium; first program instructions to derive a context object for anon-contextual data object, wherein the non-contextual data objectambiguously relates to multiple subject-matters, wherein the contextobject provides a context that identifies a specific subject-matter,from multiple subject-matters, of the non-contextual data object, andwherein the context object is derived by contextually searching andanalyzing a document, which contains multiple instances of thenon-contextual data object, to derive the context object; second programinstructions to establish a minimum validity threshold for the contextobject, wherein the minimum validity threshold defines a probabilitythat a set of one or more context objects accurately describes thecontext of the non-contextual data object; and third programinstructions to expand a range of a search area of the document untilthe minimum validity threshold is reached; fourth program instructionsto associate the non-contextual data object with the context object todefine a synthetic context-based object; fifth program instructions toassociate the synthetic context-based object with at least one specificdata store, wherein said at least one specific data store comprises datathat is associated with data contained in the non-contextual data objectand the context object; sixth program instructions to construct adimensionally constrained hierarchical synthetic context-based objectlibrary for multiple synthetic context-based objects, wherein syntheticcontext-based objects within a same dimension of the dimensionallyconstrained hierarchical synthetic context-based object library sharedata from a same non-contextual data object, and wherein syntheticcontext-based objects within the same dimension of the dimensionallyconstrained hierarchical synthetic context-based object library containdisparate data from different context objects; seventh programinstructions to receive, from a requester, a request for at least onedata store that is associated with synthetic context-based objectswithin the same dimension of the dimensionally constrained hierarchicalsynthetic context-based object library; eighth program instructions toreturn, to the requester, said at least one specific data store that isassociated with synthetic context-based objects within the samedimension of the dimensionally constrained hierarchical syntheticcontext-based object library; and wherein the first, second, third,fourth, fifth, sixth, seventh, and eighth program instructions arestored on the computer readable storage medium for execution by theprocessor via the computer readable memory.
 19. The computer system ofclaim 18, wherein the document is a video file.
 20. The computer systemof claim 18, further comprising: ninth program instructions to execute amap/reduce search of the document to reach the minimum validitythreshold, wherein the map-reduce search sequentially performsevaluations of subsequently lower-level partitions of the document todetermine finer levels of granularity to derive the context object; andtenth program instructions to utilize a progressively higher number ofprocessors to execute the evaluations of the subsequently lower-levelpartitions of the document; and wherein the ninth and tenth programinstructions are stored on the computer readable storage medium forexecution by the processor via the computer readable memory.